10 citations · 19 across the 7 of their papers we have counts for
7 papers
AdaBridge: Dynamic Data and Computation Reuse for Efficient Multi-task DNN Co-evolution in Edge Systems
Lehao Wang, Zhiwen Yu, Sicong Liu +3
Running multi-task DNNs on mobiles is an emerging trend for various applications like autonomous driving and mobile NLP. Mobile DNNs are often compressed to fit the limited resourc…
AdaOper: Energy-efficient and Responsive Concurrent DNN Inference on Mobile Devices
Zheng Lin, Bin Guo, Sicong Liu +4
Deep neural network (DNN) has driven extensive applications in mobile technology. However, for long-running mobile apps like voice assistants or video applications on smartphones,…
EchoPFL: Asynchronous Personalized Federated Learning on Mobile Devices with On-Demand Staleness Control
Xiaochen Li, Sicong Liu, Zimu Zhou +3
The rise of mobile devices with abundant sensory data and local computing capabilities has driven the trend of federated learning (FL) on these devices. And personalized FL (PFL) e…
AdaMEC: Towards a Context-Adaptive and Dynamically-Combinable DNN Deployment Framework for Mobile Edge Computing
Bowen Pang, Sicong Liu, Hongli Wang +6
With the rapid development of deep learning, recent research on intelligent and interactive mobile applications (e.g., health monitoring, speech recognition) has attracted extensiv…
AdaEvo: Edge-Assisted Continuous and Timely DNN Model Evolution for Mobile Devices
Lehao Wang, Zhiwen Yu, Haoyi Yu +4
Mobile video applications today have attracted significant attention. Deep learning model (e.g. deep neural network, DNN) compression is widely used to enable on-device inference f…
Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey
Sicong Liu, Bin Guo, Cheng Fang +4
The emerging field of artificial intelligence of things (AIoT, AI+IoT) is driven by the widespread use of intelligent infrastructures and the impressive success of deep learning (D…